São Paulo - Alberta BrainHack

Important Information:

  • BrainHack photos are available (link). If you you have a nice picture, please share with us!
  • Our speakers' slides can be downloaded (link) the password is the same as the BrainHack wifi network.
  • The conference book with all published abstracts is now available (link)
  • We have set up a github repository with machine learning demos ranging from introductory to advanced (ML101)

About the event: The São Paulo - Alberta Magnetic Resonance (MR) Image Reconstruction and Processing BrainHack workshop is in its first edition. Our goal is to join experts in their fields and young researchers to discuss contemporary MR image reconstruction and processing problems and applications using machine learning approaches. The workshop will consist of a combination of lectures, scientific power pitch presentations, BrainHack challenges and lots of opportunities for connecting with new people. You don't need to be an expert on machine learning or brain MR to participate! We will have mentors, educational lectures, and a cross-disciplinary team that will support all participants to understand machine learning, and show how it can be applied to active research problems.

Date: 17-19 October 2018

Venue: School of Electrical and Computer Engineering (FEEC) - State University of Campinas (UNICAMP), Campinas, São Paulo, Brazil.

Target audience: Graduate students, post-doctoral fellows, professors, industry and academic researchers working either on MR image acquisition, processing or analysis with interest in solving their problems through machine learning techniques. You don't need to be a programmer to participate. All areas of expertise are welcome! There is a limited number of spaces available.

Public data!

The BrainHacks will leverage the Calgary-Campinas Public Brain MR Dataset. The dataset has 359 T1-weighted brain images from multiple scanner vendors and magnetic field strengths. It keeps being constantly updated with new segmentation masks from diverse brain structures.

Large public and labeled datasets are essential for developing machine learning applications.


BrainHack Chairs:

      • Dr. Richard Frayne - University of Calgary
      • Dr. Leticia Rittner - UNICAMP

Challenge Chairs:

    • Dr. Mariana Bento - University of Calgary
    • Dr. Roberto Souza - University of Calgary

Questions about the workshop?

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